Long-Term Transitional Impact and Mental-Health Consequences of Natural Disasters: A Multi-Site Study
Bibliographic record
Abstract
Traditionally, disaster research has focused on well-being consequences or socio-economic effects, often overlooking the association between disaster-brought life changes (i.e., transition) and mental health. Therefore, in this online longitudinal survey, we aimed to evaluate the long-term transitional impact of the flood in Western Germany and the wildfire in British Columbia, Canada, both of which happened during the summer of 2021. Additionally, we aimed to examine the relationships among these disaster-specific transitions and mental health, as well as feelings of being abandoned by the community and government. In this multi-site, multi-disaster study, 48 BC and 41 Western Germany adults were first assessed in 2021, then reassessed in 2022. During both waves, respondents completed the 12-item TIS, the 21-item DASS, the 8-item PCL, and the 2-item feeling of abandonment instrument (community and government). Results indicated that (a) the Germany flood produced higher material and psychological change in 2021 than in 2022; (b) the BC fire produced higher psychological change in 2021 than 2022, but produced modest material change in both time points; (c) the BC-fire group reported greater mental distress in 2021 than 2022, the Germany-flood group reported moderate-to-severe mental distress in both waves, and neither group experienced PTSD-like symptoms; (d) in both groups, evacuees experienced more change and distress than non-evacuees; (e) BC-fire evacuees and Germany-flood non-evacuees indicated that they felt more abandoned by their community than their government; and (f) over time, only psychological changes were reliably associated with distress in both groups. We speculated that following disasters, people’s mental health was largely shaped by the levels of disaster-induced life changes, particularly psychological changes that unfold over time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".